The Electric Cargo Cycle Routing Problem: An Enhanced Approach with Predictive Battery Performance

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dc.contributor.advisorGingerich, Kevin
dc.contributor.authorGarcia Santana, Adonai Manace
dc.date.accessioned2024-07-18T21:26:00Z
dc.date.available2024-07-18T21:26:00Z
dc.date.copyright2024-04-19
dc.date.issued2024-07-18
dc.date.updated2024-07-18T21:25:59Z
dc.degree.disciplineCivil Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractThis thesis explores the optimization of electric cargo cycles (ECCs) for urban logistics, focusing on battery performance and routing efficiency. By analyzing two ECCs under different operational scenarios, a novel Electric Cargo Cycle Battery (ECCB) model is developed for battery performance forecasting. This extends to the creation of the Electric Cargo Cycle Routing Problem (ECCRP), an adaptation of the capacitated vehicle routing problem, and its Enhanced version (E-ECCRP), incorporating battery constraints and the flexibility of battery swapping. Comparative analysis of these models reveals the benefits of integrating battery performance into routing strategies, underscoring ECCs' role in eco-friendly urban logistics. This research offers valuable insights into electric mobility utilization for sustainable city transport solutions, providing a foundation for optimizing ECC usage in urban environments.
dc.identifier.urihttps://hdl.handle.net/10315/42183
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectCivil engineering
dc.subjectTransportation planning
dc.subjectUrban planning
dc.subject.keywordsElectric cargo cycle
dc.subject.keywordsCargo bike
dc.subject.keywordsElectric bike
dc.subject.keywordsLogistics
dc.subject.keywordsLast mile
dc.subject.keywordsCity logistics
dc.subject.keywordsBattery
dc.subject.keywordsRouting problem
dc.subject.keywordsOptimization routing model
dc.subject.keywordsBattery model
dc.subject.keywordsModelling
dc.subject.keywordsRandom forest
dc.subject.keywordsMachine learning
dc.subject.keywordsData-driven
dc.titleThe Electric Cargo Cycle Routing Problem: An Enhanced Approach with Predictive Battery Performance
dc.typeElectronic Thesis or Dissertation

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